Python Data Loading from hubspot
to bigquery
using dlt
Library
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This page provides technical documentation on how to load data from HubSpot
to BigQuery
using the open-source Python library, dlt
. HubSpot
, a customer relationship management (CRM) software and inbound marketing platform, aids businesses in attracting visitors, engaging customers, and closing leads. On the other hand, BigQuery
is a serverless, cost-effective enterprise data warehouse that operates across clouds and scales with your data. The dlt
library simplifies the process of transferring data from HubSpot
to BigQuery
. For more details about HubSpot
, visit https://www.hubspot.com.
dlt
Key Features
- Automated Maintenance:
dlt
offers automated maintenance with schema inference and evolution, as well as alerts. This makes maintenance simple and straightforward. More about this can be found here. - Run Anywhere Python Runs:
dlt
can run anywhere Python is supported. This includes Airflow, serverless functions, and notebooks. It does not require any external APIs, backends, or containers, and can scale on both micro and large infrastructures. Learn more here. - User-Friendly Interface:
dlt
provides a user-friendly, declarative interface that is easy to understand for beginners while also empowering senior professionals. More information can be found here. - Governance Support:
dlt
pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. This contributes to better data management practices, compliance adherence, and overall data governance. Read more here. - Community Support:
dlt
has an active community where you can ask questions, share how you use the library, and report problems or make feature requests. Join the community here.
Getting started with your pipeline locally
0. Prerequisites
dlt
requires Python 3.8 or higher. Additionally, you need to have the pip
package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.
1. Install dlt
First you need to install the dlt
library with the correct extras for BigQuery
:
pip install "dlt[bigquery]"
The dlt
cli has a useful command to get you started with any combination of source and destination. For this example, we want to load data from HubSpot
to BigQuery
. You can run the following commands to create a starting point for loading data from HubSpot
to BigQuery
:
# create a new directory
mkdir hubspot_pipeline
cd hubspot_pipeline
# initialize a new pipeline with your source and destination
dlt init hubspot bigquery
# install the required dependencies
pip install -r requirements.txt
The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt
:
dlt[bigquery]>=0.3.25
You now have the following folder structure in your project:
hubspot_pipeline/
├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── hubspot/ # folder with source specific files
│ └── ...
├── hubspot_pipeline.py # your main pipeline script
├── requirements.txt # dependencies for your pipeline
└── .gitignore # ignore files for git (not required)
2. Configuring your source and destination credentials
The dlt
cli will have created a .dlt
directory in your project folder. This directory contains a config.toml
file and a secrets.toml
file that you can use to configure your pipeline. The automatically created version of these files look like this:
generated config.toml
# put your configuration values here
[runtime]
log_level="WARNING" # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/telemetry
dlthub_telemetry = true
generated secrets.toml
# put your secret values and credentials here. do not share this file and do not push it to github
[sources.hubspot]
api_key = "api_key" # please set me up!
[destination.bigquery]
location = "US"
[destination.bigquery.credentials]
project_id = "project_id" # please set me up!
private_key = "private_key" # please set me up!
client_email = "client_email" # please set me up!
2.1. Adjust the generated code to your usecase
3. Running your pipeline for the first time
The dlt
cli has also created a main pipeline script for you at hubspot_pipeline.py
, as well as a folder hubspot
that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.
The main pipeline script will look something like this:
from typing import List
import dlt
from hubspot import hubspot, hubspot_events_for_objects, THubspotObjectType
def load_crm_data() -> None:
"""
This function loads all resources from HubSpot CRM
Returns:
None
"""
# Create a DLT pipeline object with the pipeline name, dataset name, and destination database type
# Add full_refresh=(True or False) if you need your pipeline to create the dataset in your destination
p = dlt.pipeline(
pipeline_name="hubspot",
dataset_name="hubspot_dataset",
destination='bigquery',
)
# Run the pipeline with the HubSpot source connector
info = p.run(hubspot())
# Print information about the pipeline run
print(info)
def load_crm_data_with_history() -> None:
"""
Loads all HubSpot CRM resources and property change history for each entity.
The history entries are loaded to a tables per resource `{resource_name}_property_history`, e.g. `contacts_property_history`
Returns:
None
"""
# Create a DLT pipeline object with the pipeline name, dataset name, and destination database type
# Add full_refresh=(True or False) if you need your pipeline to create the dataset in your destination
p = dlt.pipeline(
pipeline_name="hubspot",
dataset_name="hubspot_dataset",
destination='bigquery',
)
# Configure the source with `include_history` to enable property history load, history is disabled by default
data = hubspot(include_history=True)
# Run the pipeline with the HubSpot source connector
info = p.run(data)
# Print information about the pipeline run
print(info)
def load_crm_objects_with_custom_properties() -> None:
"""
Loads CRM objects, reading only properties defined by the user.
"""
# Create a DLT pipeline object with the pipeline name,
# dataset name, properties to read and destination database
# type Add full_refresh=(True or False) if you need your
# pipeline to create the dataset in your destination
p = dlt.pipeline(
pipeline_name="hubspot",
dataset_name="hubspot_dataset",
destination='bigquery',
)
source = hubspot()
# By default, all the custom properties of a CRM object are extracted,
# ignoring those driven by Hubspot (prefixed with `hs_`).
# To read fields in addition to the custom ones:
# source.contacts.bind(props=["date_of_birth", "degree"])
# To read only two particular fields:
source.contacts.bind(props=["date_of_birth", "degree"], include_custom_props=False)
# Run the pipeline with the HubSpot source connector
info = p.run(source)
# Print information about the pipeline run
print(info)
def load_web_analytics_events(
object_type: THubspotObjectType, object_ids: List[str]
) -> None:
"""
This function loads web analytics events for a list objects in `object_ids` of type `object_type`
Returns:
None
"""
# Create a DLT pipeline object with the pipeline name, dataset name, and destination database type
p = dlt.pipeline(
pipeline_name="hubspot",
dataset_name="hubspot_dataset",
destination='bigquery',
full_refresh=False,
)
# you can get many resources by calling this function for various object types
resource = hubspot_events_for_objects(object_type, object_ids)
# and load them together passing resources in the list
info = p.run([resource])
# Print information about the pipeline run
print(info)
if __name__ == "__main__":
# Call the functions to load HubSpot data into the database with and without company events enabled
load_crm_data()
load_crm_data_with_history()
load_web_analytics_events("company", ["7086461639", "7086464459"])
load_crm_objects_with_custom_properties()
Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:
python hubspot_pipeline.py
4. Inspecting your load result
You can now inspect the state of your pipeline with the dlt
cli:
dlt pipeline hubspot info
You can also use streamlit to inspect the contents of your BigQuery
destination for this:
# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline hubspot show
5. Next steps to get your pipeline running in production
One of the beauties of dlt
is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:
The Deploy section will show you how to deploy your pipeline to
- Deploy with Github Actions:
dlt
allows you to deploy your pipeline using Github Actions. This CI/CD runner is free and you can specify when the GitHub Action should run using a cron schedule expression. - Deploy with Airflow: You can also deploy your pipeline with Airflow. This method involves using Google Composer, a managed Airflow environment provided by Google.
- Deploy with Google Cloud Functions:
dlt
supports deployment using Google Cloud Functions. This serverless execution environment enables you to build and connect cloud services with code. - Other Deployment Options:
dlt
also supports other deployment options. You can learn more about these options here.
The running in production section will teach you about:
- Monitor Your Pipeline:
dlt
provides tools to monitor your data pipelines, allowing you to track the progress and status of your data loads. You can find more information on how to monitor your pipeline here. - Set Up Alerts: With
dlt
, you can set up alerts to notify you of any issues or changes in your data pipeline. This helps you to quickly identify and resolve any problems, ensuring your data pipeline runs smoothly. Check out the guide on how to set up alerts here. - Set Up Tracing: Tracing allows you to track the execution of your data pipeline, providing valuable insights into the performance and efficiency of your data loads.
dlt
supports tracing, helping you to optimize your data pipeline. You can learn how to set up tracing here.
Available Sources and Resources
For this verified source the following sources and resources are available
Source hubspot
Hubspot source provides data on companies, contacts, deals, and customer service tickets.
Resource Name | Write Disposition | Description |
---|---|---|
companies | replace | Information about organizations |
contacts | replace | Visitors, potential customers, leads |
deals | replace | Deal records, deal tracking |
products | replace | Pricing information of a product |
quotes | replace | Price proposals that salespeople can create and send to their contacts |
tickets | replace | Request for help from customers or users |
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